LLMs can be augmented to interact with the outside world by accessing APIs to load data or take actions.
1. Website integration: The chatbot could be embedded on a company’s website to provide 24/7 customer support and self-service options. This allows customers to get information or resolve issues without needing human agents.
2. Messaging platform integration: Integrating the chatbot with popular messaging apps like WhatsApp, Facebook Messenger, etc. allows customers to interact with the bot via their preferred platforms.
3. Internal systems integration: The chatbot can be integrated with internal systems like CRM, support ticketing tools, knowledge bases to retrieve customer data and provide personalized and context-aware responses.
4. Payment systems: Integration with payment gateways allows the chatbot to assist with tasks like taking payments, providing invoices/receipts, and handling refunds.
5. Enterprise software integration: Chatbots can integrate with enterprise systems like ERP, HRMS, etc. to automate tasks like providing employee benefits information, resolving payroll issues, filling complaints, etc.
6. IoT integration: In industrial environments, conversational AI can ingest data from IoT sensors and provide diagnostics or alerts when issues occur with connected devices or machines.
The core value propositions are enhanced customer and employee experience via 24/7 automated support, increased efficiency by handling high volumes of routine inquiries, and cost savings from reduced human support staffing requirements. The specific use cases would depend on the company’s needs.
Reading and aggregating data from a large document is a challenging task to perform using semantic search tooling
Doc Q&A
Reads a data source and answers a question or aggregates information on it
Summarizer
Reviews a data source and builds a summary of the content
Transcriber
Reads a data source and transcribes it using user instructions
Translator
Reads a data source and translates it from one language to another